Methods of optimising stochastic processing parameters in crop harvesting machines

ABSTRACT

The flow of crop material through a harvesting machine, such as a combine, can be described using a wide range of models, providing a relationship between the harvester load u(t), e.g. the mass flow at the inlet, and an effectiveness value y(t), e.g. the grain loss at the outlet. However, prior art models are not applicable to a wide range of harvesting conditions or are very complicated, requiring a multitude of inputs. The invention proposes to use a simple model, comprising a stochastic parameter  , which is continuously optimized to adjust the model to the latest prevailing working conditions. Such parameter may be considered to constitute a variable which characterizes the instantaneously prevailing readiness of the harvesting process. Such variable   can be used for establishing field maps showing the evolution of the harvesting operation itself. It can also be used in automatic routines which adjust the harvester speed in order to limit the grain losses.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This Patent Application claims priority under 35 U.S.C. 371 to PCTInternational Application Number PCT/EP03/08272, which was filed on Jul.25, 2003, and which has Convention Priority based on Great BritainApplication GB 0217297.1, filed Jul. 26, 2002.

FIELD OF THE INVENTION

This invention relates to methods for establishing parameters formodelling the behaviour of crop harvesting machines such as but notlimited to combine harvesters. In particular it relates to a method foroptimizing stochastic model parameters in models for a crop processingoperation in such harvesting machines. The method is particularly usefulin the context of “precision farming” techniques, in which combineharvesters and other harvesting machines have played a significant role,as well for the development as for the application of these techniques.

BACKGROUND OF THE INVENTION

“Precision farming” is a broad term that embraces practices such as:

-   -   field yield mapping aimed at accurately mapping the        crop-producing productivity of a field so that:        -   seeds and agro-chemicals can be economically and correctly            applied without over- and under-dosing the plants;        -   harvesting and other agricultural machinery can be adjusted            to take account of varying crop conditions from place to            place in a field;    -   modelling, of process conditions, within agricultural machines,        and developing control philosophies aimed at:        -   improving machine efficiency or workrate;        -   reducing fuel consumption; and        -   improving the quality of work carried out by agricultural            machines; and    -   providing alerts and reports of abnormal conditions in crops.

Sometimes the overall improvement in farming efficiency resulting froman individual precision farming practice might be only a few percent;but cumulatively such efforts have dramatically improved productivity inmechanised farming over recent years.

The significance of harvesters, such as combine harvesters, to precisionfarming derives principally from the following factors:

-   (i) A harvester is able to assess the output of a farming operation    for example in terms of crop yield. A field map of such data is    invaluable in improving farming efficiency in future crop growing    and harvesting seasons; and-   (ii) Harvesters are complicated machines that provide numerous sites    for the location of transducers whose function is to gather data on    the harvesting processes and the crops passing through the machine.

As noted, the combine harvester has been particularly useful inproducing maps indicating the expected crop yield at different locationsin a field. Farmers can use such maps (that are readily stored indigital form in a computer memory) to control in an accurate way theprocesses forming part of a crop growing season, so that the yield ofthe field is maximized.

Prior art techniques for yield mapping, however, are limited primarilybecause they concentrate on the quantity of the useable part of the cropthat is conveyed to e.g. the clean grain tank in a combine harvester.

Although measurements of e.g. the mass flowrate of clean grain to theclean grain tank may readily be compensated for some variables such asgrain moisture content and grain type, some difficulties remain.

Significant among these is the fact that mass flowrate measurements ofcrop yield generally take no account of crop losses arising from e.g.incomplete or faulty threshing of ears in the threshing drum or anotherpart of the harvester where grain separation occurs. Where the machineload exceeds the threshing, separation and/or cleaning capacity of themachine a portion of the harvested grain will not be separated from thestraw and chaff and be deposited therewith on the field behind themachine.

It is known, for example, that the extent to which (or the ease withwhich) ears are threshed in the threshing drum of a combine harvester isstrongly dependent on the feedrate of crop into the harvesting machine,when such factors as grain and straw moisture, crop variety and strawlength are kept as constants. Thus for higher feedrates relatively lessgrain is separated in the first concave threshing drum section than inthe case of low feedrates. More grain has to be separated in the furtherstages of the harvester.

Consequently for such high feedrates a lesser proportion of the crop istherefore likely to reach the clean grain tank, with the result that ayield measurement taken at such a location may be inaccurate.Furthermore such a measurement takes no account of the extent to whichgrains become damaged or lost within the combine harvester.

In reality a great number of variables influences the extent to whichthe threshing and separating sections are able to separate grain fromother plant matter such as chaff and straw. Such variables include, butare not limited to:

-   -   the nature of the soil in which the crop grows;    -   settings of various adjustable components of the harvesting        machine, e.g. the height of the header bar in a combine        harvester, which directly influences the straw to grain ratio;    -   the slope of the field in which the machine operates;    -   the moisture content of the crop;    -   the crop type;    -   the forward speed of the harvesting machine;    -   the presence of weed patches;    -   the state (wear) of the machine elements;    -   the type of installed machine elements, e.g. the type of rasp        bars; and so on.

According to a first aspect of the invention there is provided a methodof substantially continuously optimizing a stochastic parameter

that characterizes the instantaneously prevailing readiness with whichcrop is processed in a harvesting machine, including the step ofrecursively calculating the optimized parameter value in accordance withthe following algorithm:

(t)=ƒ(

(t−1),ε(t,

(t−1)))  (A)wherein:

-   -   (t) is the optimized stochastic parameter value at time t; and    -   ε(t,        (t)) is an error prediction function.

Such a method is highly suited to the continuous optimization of thehighly stochastic parameter

that, when applied to the threshing and separation process a combineharvester, may fairly be termed a “threshability” parameter, i.e. anindication of the extent to which the harvesting machine is capable ofthreshing the crop at time t.

Such a parameter is useable in various ways, as discussed hereinbelow.

The algorithm generally may take the form of:

(t)=ƒ(

(t−1), . . . ,

(t−n ),ε(t), . . . , ε(t−n _(ε)), t).

The method of the broad aspect of the invention can readily be carriedout using a suitably programmed computer carried by or forming part ofthe harvesting machine.

Preferably the algorithm (A) has the form:

(t)=

(t−1)+γ(t)r ⁻¹(t)ψ(t,

(t−1))ε(t,

(t−1))wherein

-   -   γ(t) is a gain term;    -   r(t) is a scalar approximation of a Hessian V″ (        ) in which V is indicative which V is a quadratic error        criterion;        ${{\psi( {t,\vartheta} )} = \frac{\mathbb{d}{\hat{y}( {t,\vartheta} )}}{\mathbb{d}\vartheta}},$    -   in which ŷ(t,        ) is an estimation of a value indicative of the effectiveness of        said crop processing in said harvesting machine said estimation        being based on stochastic parameter        ; and    -   ε(t,        (t−1)) is the difference between the actual effectiveness value        y(t) and the estimated value ŷ(t,        ) based on the previously optimized parameter        (t−1).

Preferably the algorithm (A) includes an estimation of r(t) that isweighted to reduce the influence, on the optimized parameter values

, of past measurements.

This aspect of the method renders the parameter optimization morerealistic and robust for a wide range of working conditions.

The parameter

may be usable in a model for the relation between a value u(t)indicative of the feedrate of crop into the harvesting machine and avalue y(t) indicative of the effectiveness of an operation processingsaid crop in said harvesting machine. The estimated value ŷ(t,

) is then an estimation of the effectiveness obtained by the applicationof said model to the feedrate values u(t).

In this manner the model can be updated continuously in order to meetany changes to the process caused by a wide range of changingconditions, e.g. varying crop properties such as ripeness or moisture orchanges in inclination of the machine.

Advantageously, the model may comprise an exponential function.

Such form provides some computational advantages for the optimization of

.

The effectiveness may take the form of a value indicative of crop flow,e.g. crop losses at the end of the separation or the cleaning section.It may also comprise the crop flow in a return system.

According to a second aspect of the invention there is provided a methodof operating a harvesting machine comprising the steps of:

-   -   substantially continuously optimizing a stochastic parameter        that characterizes the instantaneously prevailing readiness with        which the harvesting machine processes crop; and    -   substantially continuously adjusting a performance variable of        the harvesting machine in dependence on the instantaneous,        optimized value of said parameter in order to optimize the load        of the harvesting machine so as to keep a value indicative of        the effectiveness of said harvesting machine below a        predetermined value.

Such effectiveness value may comprise the losses of useable crop partssuch as separation or cleaning sieve losses, or a proportion of damageduseable crop parts, e.g. broken grain kernels, or a proportion ofunwanted material in the useable crop parts, e.g. chaff and straw andparticles in the clean grain.

Optimizing the machine load may comprise optimizing the feedrate of cropinto the harvesting machine, e.g. by adapting the travel speed of theharvesting machine.

Conveniently the step of adjusting a performance variable of theharvesting machine occurs in dependence on the output of an invertedform of an effectiveness estimation function:ŷ(t,

)=exp(

u(t))−1.  (B)Herein u(t) may be the measured feedrate and ŷ(t,

) the grain losses.

According to a third aspect of the invention there is provided a methodof mapping one or more field lots for variations in a stochasticparameter that characterizes the instantaneously prevailing readinesswith which a harvesting machine processes crop, the method comprisingthe steps of:

-   -   operating a harvesting machine to harvest crop in a said field        lot;    -   simultaneously measuring the machine load and the machine        effectiveness and determining the position of the machine in the        field lot;    -   storing data indicative of the position of the harvesting        machine at time t;    -   using the measured machine load and machine effectiveness data        in an optimization of said parameter; and    -   mapping the optimized parameter values obtained from the using        the measured machine load and machine effectiveness data step so        as to produce a parameter map of the field lot.

According to a fourth aspect of the invention there is provided a methodof operating a harvesting machine comprising the steps of:

-   -   substantially continuously optimizing a stochastic parameter        that characterizes the instantaneously prevailing readiness with        which the harvesting machine separates useable crop parts from        other plant matter; and    -   sending a display signal, that is indicative of the        instantaneous parameter value, to a display device.

Preferably the display signal indicates an abnormal parameter value.

Preferably in each of the second, third and fourth aspects of theinvention the optimization step is in accordance with the first aspectof the invention. Thus the method of the first aspect of the inventionis highly versatile in its application.

Conveniently in each of the 2^(nd) to 4^(th) aspects of the invention,when the parameter optimization is according to the first aspect of theinvention, said selected part of the harvesting machine is selectedfrom:

-   -   the separation section, e.g. the straw walkers or a rotary        separator;    -   the sieve;    -   the return flow system;    -   the cleaning section; or    -   the grain elevator;        of a combine harvester.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be described in more detail, by way ofnon-limiting example. In the following description reference is made tothe accompanying drawing figures in which:

FIG. 1 is a schematic representation of the threshing, cleaning andstraw walker sections of a combine harvester showing the possiblelocations for grain loss transducing devices;

FIG. 2 is a graphical representation of the highly stochastic nature ofa processability, in particular threshability parameter

, by reference to field slope (gradient) values;

FIG. 3 shows a graphical form the variability of the optimized parametervalue from place to place in a field undergoing harvesting. The FIG. 3graph also includes, for comparison purposes, plots of parameter valuescalculated offline using a Least Squares technique also used to generatethe plots of FIG. 2; and

FIG. 4 is a comparison between a parameter value map and the slope ofthe field to which it relates.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The terms “grain”, “straw”, and “tailings” are used principallythroughout this specification for convenience and it should beunderstood that these terms are not intended to be limiting. Thus“grain” refers to that part of the crop which is threshed and separatedfrom the discardable part of the crop material which is referred to as“straw”. Incompletely threshed ears are referred to as “tailings”. Also,the terms “forward”, “rearward”, “upper”, “lower”, “left”, “right”, etc.when used in connection with the combine harvester and/or componentsthereof, are determined with reference to the combine harvester in itsnormal operational condition and may refer either to the direction offorward operative travel of the combine harvester or to the direction ofnormal material flow through components thereof. These terms should notbe construed as limiting.

FIG. 1 shows the position of respective crop flow or loss sensors 10,11, 12 in the separation 13, cleaning 1) and return flow 15 sections ofa typical combine harvester. The illustrated separation sectioncomprises a set of straw walkers 13, but the invention may also be usedwith harvesters comprising a rotary separator.

Any of the positions 10, 11, 12 may readily be used within the scope ofthe invention as locations for grain loss sensors. The following notesapply to the respective sensors:

Sensor 10:

Separation losses are generally measured by means of impact detectors.Impacts of kernels are separated from impacts of straw ends and countedeach second. By this, a measure is found for the amount of separationlosses. Since the distribution of free grain kernels along the depth ofthe straw layer varies in time, only a rough indication is normallyobtained.

Sensor 11:

One similar impact sensor may be installed at the end of the cleaningsection. Again only a rough measure is normally found for the sievelosses.

Sensor 12:

Tailings fall behind the lower sieve into the cross auger of the returnflow system. An impeller is installed at the end of the fast rotatingauger to spread the return flow. The sensor preferably operatesmeasuring the phase shift between a fixed impeller and a flexible one asdescribed in EP-A-0 463 240. Higher material flows imply larger phaseshifts.

In addition, the harvester preferably is equipped with precision farmingtools to measure the actual cutting width, ground speed, mass flow,feedrate and position within the field.

The outputs of such sensors may be employed in a technique, involvingthe use of an offline model for the crop processing operation.Establishing such model involves the determination of one or moreparameters, e.g. a threshability parameter

.

The exponential function offers good opportunities to make a predictionŷ(t,

) of actual separation losses y(t) (T/h) based on an online measurementof feedrate u(t) (T/h). The feedrate may be measured at the inlet of theharvester, e.g. by monitoring the volume of crop material entering thestraw elevator, or by measuring the force or torque needed to convey thecrop mass into the machine.

Advantageously, the exponential function may take the form of:{circumflex over (y)}(t,

)=exp(

u(t))−1.  (1)Equation (1) illustrates that no separation losses occurs for a zerofeedrate and the feedrate-loss relation is fully determined by parameter

. The time delay between the feedrate signal u(t) and y(t) is assumed tobe fixed and an optimal fixed time shift (typically 11 s) is installedas a compensation. For online measurements, separation losses andfeedrate will be expressed in respectively impacts per second (#/s) andVolt (V). To optimize the parameter

, following quadratic criterion V(

) is proposed in function of prediction error ε(t,

) (#/s):V(

)=E{0.5ε²(t,

)} ε(t,

)=y(t)−{circumflex over (y)}(t,

)  (2)

In case an optimal solution has to be found after N discrete input andoutput measurements (u_(k), y_(k)) are carried out, V(

) can be approximated by V_(N)(

): $\begin{matrix}{{V_{N}(\vartheta)} = {\frac{1}{N}{\sum\limits_{k = 1}^{N}{0.5{{ɛ^{2}( {k,\vartheta} )}.}}}}} & (3)\end{matrix}$

For each set of N measurements it is possible to find an optimalparameter

wherefor the quadratic criterion V(

) is minimised. FIG. 2 gives the optimized exponential relation for fivesections in one 330 m harvest strip with a large variance inthreshability due to different nitrogen applications and slope (α)fluctuations. In the first and last section, some erratic data pointsare lying near to the X and Y-axis respectively because of the dynamicstep responses when the harvester enters and leaves the crop.

Recursive Estimation of Grain Loss Curves

As can be seen in FIG. 2, a large variance in the model parameter can bedetected in one single crop strip. Therefore, it is preferable to havean online optimal estimation (in accordance with the invention) of thelocal separation behaviour. This site-specific information offers newpossibilities for automatic tuning systems in a more direct way comparedto the prior art online yield mapping systems, since it immediatelyrelates feedrate u(t) with straw walker losses y(t) and provides anextra crop parameter

that may play an important role in the evaluation of new crop varieties.

Herein an online optimization procedure is derived based on a stochasticgradient method. The stochastic gradient method can be seen as astochastic analog of the method of steepest descent for the minimisationof a deterministic function. In its general form, this method ofsteepest descent is represented by: $\begin{matrix}{x^{({t + 1})} = {{x^{(t)} - {\gamma^{(t)}\lbrack {\frac{\mathbb{d}}{\mathbb{d}x}{V(x)}} \rbrack}^{T}}❘_{x = x^{(t)}}}} & (4)\end{matrix}$where x^((t)) denotes the t th iterate and γ^((t)) a chosen positivescalar. This approach may show useful, however, when the iterates aregetting close to the minimum, this method is known to be fairlyinefficient.

The so-called quasi-Newton methods yield distinctly better results anduse a modified search direction from the negative gradient direction:$\begin{matrix}{x^{({t + 1})} = {{x^{(t)} - {{\gamma^{(t)}\lbrack {\frac{\mathbb{d}^{2}}{\mathbb{d}x^{2}}{V(x)}} \rbrack}^{- 1}\lbrack {\frac{\mathbb{d}}{\mathbb{d}x}{V(x)}} \rbrack}^{T}}❘_{x = x^{(t)}}}} & (5)\end{matrix}$This iteration will provide convergence in one step to the minimum ofV(x), if this function is quadratic in x and γ^((t))≡0.5.

When applied to the present optimization problem, the quasi-Newtonoptimization scheme can be transformed into following gradient scheme,which could be called a “stochastic Newton algorithm”:V(

)=EH(

,e(t))  (6)

(t)=

(t−1)+γ(t)[{overscore (V)}″(

(t−1),e ^(t))]⁻¹ Q(

(t−1),e ^(t))  (7)where {overscore (V)}″(.) denotes the approximate Hessian, −Q(.) is thegradient of H(x,e) with respect to x and e^(t) indicates that theapproximation depends on previous noise values e^(t)=e(t), e(t−1), . . .. When this scheme is applied to the problem definition of equation (1)and (2), following algorithm is obtained for the model parameter

(t):

(t)=

(t−1)+γ(t)r ⁻¹(t)ψ(t,

(t−1))ε(t,

(t−1))  (8)where${\psi( {t,\vartheta} )} = \frac{\mathbb{d}{\hat{y}( {t,\vartheta} )}}{\mathbb{d}\vartheta}$and scalar r(t) corresponds with the instantaneous estimation of HessianV″(

). When an exponential process model according to function (1) has beenchosen, ψ(t,

) will be equal to exp(

.u(t)).u(t).

The accuracy of this approximation of the true Hessian plays aparticularly important part when the recursive algorithm is operatingclose to the minimum. The natural approximation of this Hessian is tointroduce the sample mean value: $\begin{matrix}{{\frac{\mathbb{d}^{2}}{\mathbb{d}\vartheta^{2}}{V(\vartheta)}}\overset{\Delta}{=}{{{E\{ {\psi^{2}( {t,\vartheta} )} \}} \approx {\frac{1}{t}{\sum\limits_{k = 1}^{t}{\psi^{2}( {t,{\hat{\vartheta}( {t - 1} )}} )}}}}\overset{\Delta\quad}{=}{r(t)}}} & (9)\end{matrix}$

However, the estimation of r(t) in equation (9) puts as much attentionon measurements temporally far from t as on more recent measurements.Therefore, a weighted estimation of r(t) usually yields better results:$\begin{matrix}{{r(t)} = {{\sum\limits_{k = 1}^{t}{{\beta( {t,k} )}{\psi^{2}( {k,{\hat{\vartheta}( {k - 1} )}} )}}} + {{\delta(t)}r_{0}}}} & (10)\end{matrix}$where r₀ denotes the initial estimation of the Hessian function andweighting coefficients β(t,k) and δ(t) should be chosen such that$\begin{matrix}{{{\sum\limits_{k = 1}^{t}{\beta( {t,k} )}} + {\delta(t)}} \equiv {1\quad{\forall{t.}}}} & (11)\end{matrix}$

A standard way to define the weighting coefficients is given by$\begin{matrix}{{{\gamma(t)} = \gamma_{0}}\quad{{\delta(t)} \equiv {\prod\limits_{k = 1}^{t}\lbrack {1 - \gamma_{0}} \rbrack}}\quad{{\beta( {t,k} )} \equiv {\prod\limits_{j = {k + 1}}^{t}{\lbrack {1 - \gamma_{0}} \rbrack.}}}} & (12)\end{matrix}$It is easy to verify that this choice of parameters fulfils condition(11). Constant gain parameter γ₀ corresponds to an exponentialforgetting factor λ₀≡1−γ₀.

A preferred but non-limiting practical realisation of this stochasticNewton algorithm is shown hereinafter:r(0)=r ₀;

(0)=

₀;k=1;while (k<N);ψ(k)=u(k)*exp[u(k)*

(k−1)];ε(k)=y(k)−exp[u(k)*

(k−1)]+1;r(k)=r(k−1)+γ₀*[ψ²(k)−r(k−1)];

(k)=

(k−1)+γ₀*ψ(k)*ε(k)/r(k);k++;end;

The algorithm is computationally cheap and can easily be implemented inmachine software. Three parameters have to be determined before startingthe algorithm. The preferred but non-limiting default values that areused in this study are added between brackets.

-   1. Gain sequence γ(k) is set at a constant value γ₀ (0.2). This    parameter immediately determines the tracking capabilities of the    algorithm but has also an important influence on the variance on the    estimation of parameter    (k). A high constant gain brings about fast tracking dynamics to    follow fluctuations of    (k), but introduces large fluctuations around the true    (k), even when the true parameter does not vary in time. It depends    on the application purposes and sample frequency whether a high or    low gain should be chosen.-   2. The initial estimation r₀ (500) of the Hessian function    determines the confidence of having an accurate initial estimation    of    (k) A high value of r₀ implies a long term effect of the initial    estimation    ₀ on the following parameter estimations.-   3. Different strategies can be introduced to choose the initial    parameter estimation    ₀ (8). The last parameter value of a previous harvest run can be    used or a parameter estimation of a near by crop strip when the    tracking algorithm is connected to a positioning system.

Results and Discussion

The algorithm that has been developed in the previous section can beused to track the relation between all combinations of process (e.g.separation) and feedrate sensors. Hereabove, the relation betweenfeedrate and straw walker losses has been studied in more detail, sinceit may be used in automatic tuning systems that control the straw walkerlosses by adapting the feedrate. When the static non-linearcharacteristic between both signals can be tracked online, a groundspeed control system can be realised that keeps the straw walker lossesat a predefined level. When such knowledge about the instantaneousseparation behaviour would not be available, more conservativecontrollers have to be designed, resulting in lower controlperformances.

In FIG. 2, the signals of one 330 m harvest strip were divided into fivedistinct sections of about 66 m each. For each section, an optimalexponential parameter

is calculated based on offline optimization techniques. FIG. 3 shows theresults of the recursive algorithm for the same harvest strip. Theresults of the previously described offline optimization procedure arealso shown to illustrate the performance of the tracking algorithm.Roughly the same parameters are obtained, but now in an online,recursive way. The zone between 50 and 150 m corresponds to data from anuphill section (+15%). Harvesting uphill reduces the separation capacityof conventional harvesters and as a result, larger coefficients

are obtained. Similar results are found for zones with smallergrain/straw ratios or higher moisture contents.

Site-Specific Interpretation of Estimated Parameter Sequence

In case variations of parameter

depend on local field conditions, the same trend should be visible inadjacent strips. FIG. 4 shows this type of parameter map for a set of 7runs that are harvested uphill, parallel to the Y-axis. The correlationwith the field slope or machine slope is evident, illustrating the valueof this type of parametric maps for on-line tuning of harvesters, beforevariations in local processability are actually registered. This fieldwas fertilised with different doses of Nitrogen in strips parallel tothe Y-axis. Therefore, the harvester reacts in a slightly different wayaccording to the Nitrogen gift and by this, to its position on theX-axis. A field with a constant Nitrogen application would give an evenbetter correlation of the parameter and slope map.

Although in the foregoing discussion the relationship between feedrateu(t) and straw walker losses y(t) is described, the method of theinvention could equally well be used for optimizing parameters

in models for other crop handling processes at any of the followingsections of a combine harvester:

-   -   the separation section, e.g. the straw walker or the separator        rotor;    -   the sieves;    -   the return flow system; or    -   the cleaning section;        or indeed in sections of other kinds of harvester.

A processability parameter can also be used for predicting the behaviourof the harvesting machine with respect to other effectiveness valuessuch as the proportion of damaged useable crop parts, e.g. broken grainkernels, or a proportion of unwanted material in the useable crop parts,e.g. chaff and straw and particles in the clean grain.

The ability of the method of the invention to optimize the parameter

in an online way offers numerous potential advantages, such as:

-   -   the ability to devise a reliable vehicle control system that        controls a performance variable of the harvesting machine and        that relies on a feedrate set point calculated by an inverted        form of Equation (1);    -   the ability to devise processability-based expert systems, e.g.        diagnostic systems that are self-executing in a combine        harvester; warning systems manifested as feedrate or parameter        “out of range” values; or information systems such as        gradient-related data about likely crop losses.

A further advantage of the methods of the invention is that theprocessability parameter

can if desired be assessed largely independently of the various sensingsubsystems of a harvesting machine equipped for precision farming. Inother words the optimized threshability parameter

implicitly takes account of the crop, vehicle and field conditions thatgive rise to a particular value of

, without necessarily having to evaluate each individual cause.

The method described in full detail above can be used for optimizing asingle parameter used in a model for crop processing. However, it isreadily conceivable that analogous methods can be used for optimizingtwo or more stochastic parameters where such plurality of parameters isused for modelling an operation in a harvesting machine. Each parameterby itself, or each combination of parameters, can constitute acharacteristic of the readiness with which the crop is processed.

1. A method of substantially continuously optimizing a stochasticparameter

that characterizes the instantaneously prevailing readiness with whichcrop is processed in a harvesting machine, including the step ofrecursively calculating the optimized parameter value in accordance withthe following algorithm:

(t)=ƒ(

(t−1),ε(t,

(t−1)))  (A) wherein:

(t) is the optimized stochastic parameter value at time t; and ε(t,

(t)) is an error prediction function.
 2. A method according to claim 1,wherein the algorithm (A) has the form:

(t)=ƒ(

(t−1), . . . ,

(t−n ),ε(t), . . . , ε(t−n _(ε)),t)
 3. A method according to claim 1,wherein the algorithm (A) has the form:

(t)=

(t−1)+γ(t)r ⁻¹(t)ψ(t,

(t−1))ε(t,

(t−1)) wherein: γ(t) is a gain term; r(t) is a scalar approximation of aHessian V″(

) in which V is a quadratic error criterion;${{\psi( {t,\vartheta} )} = \frac{\mathbb{d}{\hat{y}( {t,\vartheta} )}}{\mathbb{d}\vartheta}},$in which ŷ(t,

) is an estimation of a value indicative of the effectiveness of cropprocessing in said harvesting machine, said estimation being based onstochastic parameter

; and ε(t,

(t−1)) is the difference between the actual effectiveness value y(t) andthe estimated value ŷ(t,

) based on the previously optimized parameter

(t−1).
 4. A method according to claim 3, wherein the algorithm (A)includes an estimation of r(t) that is weighted to reduce the influence,on the optimized parameter values

, of past measurements.
 5. A method according to claim 3, wherein: saidstochastic parameter

is usable in a model for the relation between a value u(t) indicative ofthe feedrate of crop into the harvesting machine and a value y(t)indicative of the effectiveness of an operation processing said crop insaid harvesting machine; and said value ŷ(t,

) is an estimation value of the effectiveness obtained by theapplication of said model to the feedrate values u(t).
 6. A methodaccording to claim 5, wherein said model comprises an exponentialfunction.
 7. A method according to claim 6, wherein said model has theform:ŷ(t,

)=exp(

u(t))−1.  (B)
 8. A method according to claim 5, wherein: said cropprocessing comprises separating useable crop parts from other plantmatter; and said value y(t) is indicative of a flow of useable croplosses in a selected part of the harvesting machine.
 9. A methodaccording to claim 5, wherein: said crop processing operation comprisesseparating useable crop parts from other plant matter; and said valuey(t) is indicative of a flow of return crop in a selected part of theharvesting machine.
 10. A method of operating a harvesting machinecomprising the steps of: substantially continuously optimizing astochastic parameter

that characterizes the instantaneously prevailing readiness with whichthe harvesting machine processes crop; and substantially continuouslyadjusting a performance variable of the harvesting machine in dependenceon the instantaneous, optimized value

of said parameter in order to optimize the load of the harvestingmachine so as to keep a value y(t) indicative of the effectiveness ofsaid harvesting machine below a predetermined value.
 11. A methodaccording to claim 10, wherein: processing the crop comprises separatinguseable crop parts from other plant matter; optimizing the load of theharvesting machine comprises optimizing the feedrate u(t) of crop intothe harvesting machine; and the effectiveness value comprises lossesy(t) of useable crop parts.
 12. A method according to claim 10, whereinthe step of continuously optimizing a stochastic parameter

includes carrying out the method steps of claim
 1. 13. A methodaccording to claim 10, wherein the step of adjusting a performancevariable of the harvesting machine occurs in dependence on the output ofan inverted form of a yield loss estimation function:{circumflex over (y)}(t,

)=exp(

u(t))−1.  (B)
 14. A method according to claim 10, wherein adjusting aperformance variable comprises adjusting the travel speed of saidharvesting machine or the actual cutting width of a header of saidharvesting machine.
 15. A method of mapping one or more field lots forvariations in a stochastic parameter

that characterizes the instantaneously prevailing readiness with whichcrop is processed in a harvesting machine, the method comprising thesteps of: operating a harvesting machine to harvest crop in a field lot;simultaneously measuring the machine load and the machine effectivenessand determining the position of the machine in the field lot; storingdata indicative of the position of the harvesting machine at time t;using the measured machine load data u(t), and machine effectivenessdata y(t) in an optimization of said parameter

; and mapping the optimized parameter values

obtained from the step of using the measured machine load data u(t) andmachine effectiveness data y(t) in an optimization of said parameter

; as to produce a parameter map of the field lot.
 16. A method accordingto claim 15, wherein the step of using the measured machine load datau(t) and machine effectiveness data y(t) in an optimization of saidparameter

includes carrying out an optimization according to claim
 1. 17. A methodof operating a harvesting machine comprising the steps of: substantiallycontinuously optimizing a stochastic parameter

that characterizes the instantaneously prevailing readiness with whichthe harvesting machine separates useable crop parts from other plantmatter; and sending a display signal, that is indicative of theinstantaneous parameter value

, to a display device.
 18. A method according to claim 17, wherein thestep of optimizing a stochastic parameter

includes carrying out the method of claim
 1. 19. A method according toclaim 17, wherein the display signal indicates an abnormal parametervalue

.
 20. A method according to claim 1, wherein said harvesting machine isa combine harvester and the crop is a grain-bearing plant.
 21. A methodaccording to claim 8, wherein said selected part of the harvestingmachine is: the straw walkers; the rotary separator; the sieves; thegrain elevator; the return flow system; the cleaning section; or theaxial threshing and separating rotor; of a combine harvester.